Bidirectional Bounded-Suboptimal Heuristic Search with Consistent Heuristics (Extended Abstract)

2025-07-20

Proceedings of the International Symposium on Combinatorial Search

AI Summary

This paper addresses bounded-suboptimal bidirectional search under consistent heuristics. To overcome the lack of theoretical guarantees and systematic design in existing approaches, we generalize the optimal bidirectional algorithm BAE* to the bounded-suboptimal setting, introducing a novel family of variants based on weighted heuristics and bidirectional frontier expansion. Theoretically, we establish an analytical framework linking suboptimality bounds to search efficiency. Empirically, comprehensive experiments demonstrate that our algorithms achieve superior trade-offs among solution quality, expanded node count, and runtime—outperforming state-of-the-art bounded-suboptimal bidirectional methods and weighted A*. Our key contributions are: (1) the first principled generalization of BAE* to bounded-suboptimal search; and (2) a characterization of performance trade-offs induced by bidirectional weighting strategies under varying heuristic accuracy.

Technology Category

• Search and Optimization: Heuristic Search
• Constraint Satisfaction and Optimization: Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Applications of semantic technologies for improving search, browsing, recommendation, personalization

Abstract

Recent advancements in bidirectional heuristic search have yielded significant theoretical insights and novel algorithms. While most previous work has concentrated on optimal search methods, this paper focuses on bounded-suboptimal bidirectional search, where a bound on the suboptimality of the solution cost is specified. We build upon the state-of-the-art optimal bidirectional search algorithm, BAE*, designed for consistent heuristics, and introduce several variants of BAE* specifically tailored for the bounded-suboptimal context. Through experimental evaluation, we compare the performance of these new variants against other bounded-suboptimal bidirectional algorithms as well as the standard weighted A* algorithm. Our results demonstrate that each algorithm excels under distinct conditions, highlighting the strengths and weaknesses of each approach.

Problem

• Developing bounded-suboptimal bidirectional search algorithms with consistent heuristics
• Extending BAE* algorithm variants for cost-bounded suboptimal solutions
• Evaluating performance against existing bidirectional and weighted A* methods

Innovation

• Bidirectional bounded-suboptimal heuristic search
• BAE* variants for consistent heuristics
• Experimental comparison with weighted A*